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Bases: BaseSynthesizer

Source code in ydata/sdk/synthesizers/regular.py 
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class RegularSynthesizer(BaseSynthesizer):

    def sample(self, n_samples: int = 1) -> pdDataFrame:
        """Sample from a [`RegularSynthesizer`][ydata.sdk.synthesizers.RegularSynthesizer]
        instance.

        Arguments:
            n_samples (int): number of rows in the sample

        Returns:
            synthetic data
        """
        if n_samples < 1:
            raise InputError("Parameter 'n_samples' must be greater than 0")

        return self._sample(payload={"numberOfRecords": n_samples})

    def fit(self, X: Union[DataSource, pdDataFrame],
            privacy_level: PrivacyLevel = PrivacyLevel.HIGH_FIDELITY,
            entity_id_cols: Optional[Union[str, List[str]]] = None,
            generate_cols: Optional[List[str]] = None,
            exclude_cols: Optional[List[str]] = None,
            dtypes: Optional[Dict[str, Union[str, DataType]]] = None,
            target: Optional[str] = None,
            name: Optional[str] = None,
            anonymize: Optional[dict] = None) -> None:
        """Fit the synthesizer.

        The synthesizer accepts as training dataset either a pandas [`DataFrame`][pandas.DataFrame] directly or a YData [`DataSource`][ydata.sdk.datasources.DataSource].

        Arguments:
            X (Union[DataSource, pandas.DataFrame]): Training dataset
            privacy_level (PrivacyLevel): Synthesizer privacy level (defaults to high fidelity)
            entity_id_cols (Union[str, List[str]]): (optional) columns representing entities ID
            generate_cols (List[str]): (optional) columns that should be synthesized
            exclude_cols (List[str]): (optional) columns that should not be synthesized
            dtypes (Dict[str, Union[str, DataType]]): (optional) datatype mapping that will overwrite the datasource metadata column datatypes
            target (Optional[str]): (optional) Target column
            name (Optional[str]): (optional) Synthesizer instance name
            anonymize (Optional[str]): (optional) fields to anonymize and the anonymization strategy
        """
        BaseSynthesizer.fit(self, X=X, datatype=DataSourceType.TABULAR, entity_id_cols=entity_id_cols,
                            generate_cols=generate_cols, exclude_cols=exclude_cols, dtypes=dtypes,
                            target=target, name=name, anonymize=anonymize, privacy_level=privacy_level)

    def __repr__(self):
        if self._model is not None:
            return self._model.__repr__()
        else:
            return "RegularSynthesizer(Not Initialized)"

fit(X, privacy_level=PrivacyLevel.HIGH_FIDELITY, entity_id_cols=None, generate_cols=None, exclude_cols=None, dtypes=None, target=None, name=None, anonymize=None)

Fit the synthesizer.

The synthesizer accepts as training dataset either a pandas DataFrame directly or a YData DataSource.

Parameters:

Name Type Description Default
X Union[DataSource, pandas.DataFrame]

Training dataset

 required
privacy_level PrivacyLevel

Synthesizer privacy level (defaults to high fidelity)

 PrivacyLevel.HIGH_FIDELITY
entity_id_cols Union[str, List[str]]

(optional) columns representing entities ID

 None
generate_cols List[str]

(optional) columns that should be synthesized

 None
exclude_cols List[str]

(optional) columns that should not be synthesized

 None
dtypes Dict[str, Union[str, DataType]]

(optional) datatype mapping that will overwrite the datasource metadata column datatypes

 None
target Optional[str]

(optional) Target column

 None
name Optional[str]

(optional) Synthesizer instance name

 None
anonymize Optional[str]

(optional) fields to anonymize and the anonymization strategy

 None
Source code in ydata/sdk/synthesizers/regular.py 
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def fit(self, X: Union[DataSource, pdDataFrame],
        privacy_level: PrivacyLevel = PrivacyLevel.HIGH_FIDELITY,
        entity_id_cols: Optional[Union[str, List[str]]] = None,
        generate_cols: Optional[List[str]] = None,
        exclude_cols: Optional[List[str]] = None,
        dtypes: Optional[Dict[str, Union[str, DataType]]] = None,
        target: Optional[str] = None,
        name: Optional[str] = None,
        anonymize: Optional[dict] = None) -> None:
    """Fit the synthesizer.

    The synthesizer accepts as training dataset either a pandas [`DataFrame`][pandas.DataFrame] directly or a YData [`DataSource`][ydata.sdk.datasources.DataSource].

    Arguments:
        X (Union[DataSource, pandas.DataFrame]): Training dataset
        privacy_level (PrivacyLevel): Synthesizer privacy level (defaults to high fidelity)
        entity_id_cols (Union[str, List[str]]): (optional) columns representing entities ID
        generate_cols (List[str]): (optional) columns that should be synthesized
        exclude_cols (List[str]): (optional) columns that should not be synthesized
        dtypes (Dict[str, Union[str, DataType]]): (optional) datatype mapping that will overwrite the datasource metadata column datatypes
        target (Optional[str]): (optional) Target column
        name (Optional[str]): (optional) Synthesizer instance name
        anonymize (Optional[str]): (optional) fields to anonymize and the anonymization strategy
    """
    BaseSynthesizer.fit(self, X=X, datatype=DataSourceType.TABULAR, entity_id_cols=entity_id_cols,
                        generate_cols=generate_cols, exclude_cols=exclude_cols, dtypes=dtypes,
                        target=target, name=name, anonymize=anonymize, privacy_level=privacy_level)

sample(n_samples=1)

Sample from a RegularSynthesizer instance.

Parameters:

Name Type Description Default
n_samples int

number of rows in the sample

 1

Returns:

Type Description
pdDataFrame

synthetic data
Source code in ydata/sdk/synthesizers/regular.py 
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def sample(self, n_samples: int = 1) -> pdDataFrame:
    """Sample from a [`RegularSynthesizer`][ydata.sdk.synthesizers.RegularSynthesizer]
    instance.

    Arguments:
        n_samples (int): number of rows in the sample

    Returns:
        synthetic data
    """
    if n_samples < 1:
        raise InputError("Parameter 'n_samples' must be greater than 0")

    return self._sample(payload={"numberOfRecords": n_samples})

PrivacyLevel

Bases: Enum

Privacy level exposed to the end-user.

BALANCED_PRIVACY_FIDELITY = auto() class-attribute

Balanced privacy/fidelity

HIGH_FIDELITY = auto() class-attribute

High fidelity

HIGH_PRIVACY = auto() class-attribute

High privacy